Example embodiments of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and a computer-implemented method for providing an interactive and social fitting room experience. Consistent with some embodiments, the method may include selecting a target audience to poll for feedback on an image of an individual wearing a garment in a fitting room. The method may further include transmitting the image to a computing device of each member of the target audience, and compiling feedback regarding the image from members of the target audience. The method may further include causing the compiled feedback to be displayed to the individual on an interactive mirror display in the fitting room.

A method, system and product including obtaining offline user information at an end device, wherein the offline user information is obtained from offline sensors of the end device; based on the offline user information, generating a user profile indicating that a user of the end device matches at least one micro-segment, wherein the at least one micro-segment comprises at least one detailed population category; based on the at least one micro-segment, selecting a campaign from a set of one or more campaigns retained at a server, wherein the campaign comprises one or more rules for displaying at least one content item; monitoring the offline sensors of the end device to identify real time user activities; and upon identifying, based on the real time user activities, that a rule of the one or more rules for displaying a content item is complied with, displaying the content item in the end device.

Systems and methods are provided for generating an advertisement for output based on an item on a wish list. A request to obtain media content is received at a computing device, and a user profiled associated with the media content is identified. A wish list associated with the user profile is accessed, and an item on the wish list is identified. A first advertisement is identified, wherein the first advertisement is based on the identified item. A representation of the first advertisement and the media content are received at the computing device. A representation of the first advertisement and the media content are generated for output at the computing device.

Techniques for generating recommendations using machine learning with respect to semantic concepts defined in a knowledge graph. A hair profile is determined for a user based on inputs related to the user. Determining the hair profile includes extracting attributes of the user from the inputs using natural language processing, computer vision, or both, and identifying respective nodes for the extracted attributes in the knowledge graph. The knowledge graph is created via machine learning using population data including hair-related data in order to identify relationships between semantic concepts represented by nodes of the knowledge graph. The nodes include discrete properties such as individual hair attributes, ingredients of products, or otherwise discrete characteristics of factors that may affect a user's hair or related health conditions. A generalized recommendation is generated based on the hair profile. A personalized recommendation may be generated based on the generalized recommendation and progress logged by the user.

An automatic system facilitates selection of media properties on which to display an advertisement, responsive to a profile collected on a first media property, where a behavioral-targeting company calculates expected profit for an ad correlated with the profile and arranges for the visitor to be tagged with a tag readable by the selected media property. The profit can be calculated by deducting, from the revenues that are expected to be generated from an ad delivered based on the collected profile, at least the price of ad space at a media property where the BT company might like to deliver ads to the profiled visitor. When the calculated profit is positive (i.e., not a loss), the BT company arranges for the visitor to be tagged with a tag readable by the selected media property through which the BT company expects to profit.

An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

Systems and methods of the present disclosure allow associating rendered third-party content to respective conversions. A data processing system can provide a third-party content item for rendering on a first client computing device and store information indicative of such rendering. The data processing system can detect a realization action associated with a second client computing device and correlating the realization action with the rendering of the third-party content item based on the stored information indicative of the rendering. The data processing system can then update a realization action parameter associated with the third-party content item in response to correlating the realization action with the rendering of the third-party content item.

Systems and methods are shown for providing private local sponsored content selection and improving intelligence models through distribution among mobile devices. This allows greater data gathering capabilities through the use of the sensors of the mobile devices as well as data stored on data storage components of the mobile devices to create predicted models while offering better opportunities to preserve privacy. Locally stored profiles comprising machine intelligence models may also be used to determine the relevance of the data gathered and in improving an aggregated model for identifying the relevance of data and the selection of sponsored content items. Distributed optimization is used in conjunction with privacy techniques to create the improved machine intelligence models. Publishers may also benefit from the improved privacy by protecting the statistics of type or volume of sponsored content items shown with publisher content.

Novel system, methods, which include machine learning, and device for providing color and fashion recommendations, including for persons with visual impairment such as color blindness or complete blindness. Also, methods providing a data storage system for storing digital renditions of garments; providing a portable communication device to extract color and/or pattern from garments through use of a camera and at least one algorithm; providing a processor capable of accessing locally stored and/or remote information about or learning the preferred matching set of garments; assigning each garment in the set of garments a red-green-blue (RGB) value; providing a suitability ranking for matching compatibility of the garment or the set of garments; and providing recommendations for preferred matching garment or set of garments by organizing the garments in at least one queue selected from the group consisting of audial, tactile, visual or a combination thereof, wherein the individual imports garments or set of garments, through a series of photos or video, for bulk import into a virtual closet for the identification and assignment of type of garments or set of garments using human or computational methods.

Aspects of the subject disclosure may include, for example, identifying a first communication device associated with a first user, identifying, based on the identifying of the first communication device associated with the first user, at least one profile associated with at least a second user that is different from the first user, performing a search for content based on the at least one profile, generating search results based on the search, and providing the search results to the first communication device. Other embodiments are disclosed.